It's A. Question says "CLEANED and normalized data". All the other options are examples of data that is only normalized. Cleaning includes getting rid of duplicate info - Normalizing CAN entail that task as well.
Tough one but i will go with D
Data Cleaning: involves identifying and correcting errors, inconsistencies, and inaccuracies in the dataset. This process ensures that the data is reliable and devoid of any anomalies that might adversely affect downstream analyses.
Data Normalization, on the other hand, is the process of organizing data into a standardized format, eliminating redundancy and improving data consistency. It involves structuring and designing databases according to specific normalization rules, such as the widely used Normal Forms (NFs).
The auditor ensured data fields were consistent and that data could be used for a specific purpose." This aligns with both data cleaning and normalization processes, as it emphasizes ensuring consistency and usability for specific analytical purposes.
after another review i think the answer is C, Cleaning and normalizing data is all about transforming raw, messy data into a reliable and consistent format that's ready for analysis. By identifying and correcting anomalies—such as errors, inconsistencies, or missing values—the auditor ensures the data is accurate and fit for its intended analytical purpose.
Now I'm thinking it could be 'D' based on the article. 'A' is too simple and applies to CLEANING data. 'B' doesn't make sense. 'C' is not right because you're not supposed to identify and then correct any anomalies - you're supposed to look for anomalies AFTER you clean/normalize the data and then find the cause for the anomalies. According to that Reference article, the one that answers the question of what BEST describes the auditor's purpose for cleaning/normalizing data (in other words: WHY did the auditor clean/normalize data), it makes sense to say that that "The auditor cleaned and normalized the data so that the data fields were consistent and could be used for a specific purpose".
D is regard consistency of data so not correct.
Cleansing data includes identifying and removing duplicate data and identifying whether identically named data fields from different systems have identical or different meanings.
Normalizing data is the process of organizing data in order to reduce the potential of redundancy and to facilitate the use of the data for specific purposes. Normalizing also allows for the identification of anomalies, which might represent actual problems or potential opportunities.
so the answer is C
The only problem though is that 'C' says for anomalies to be identified AND CORRECTED. If it didn't have the "and corrected" portion to the answer, I'd agree with you. But it says "anomalies were identified and corrected". Normalization does not include correcting the anomalies. The article states that outliers (another term for anomalies) need to be investigated for the cause AFTER the data has been cleaned/normalized. Nowhere does it state to CORRECT them.
Although D is definitely not overly comprehensive, it does answer the reason/purpose for why an internal auditor cleaned/normalized data.
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